similar to: bootstrapping in regression

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

Hello All, Despite my preference for reporting confidence intervals, I need to obtain a p-value for a hypothesis test in the context of regression using bootstrapping. I have read John Fox's chapter on bootstrapping regression models and have consulted Efron & Tibshirani's "An Introduction to the Bootstrap" but I just wanted to ask the experts here for some feedback to make

The other day I was looking into one of the classics in resampling, Eugene Edgington's "Randomization Tests". This type of test is simple to do in R with things like a simple correlation, the sample () function is perfect for the purpose. However, things are more complex if you have grouped data, like a one-way ANOVA. The reason is that you have to avoid the consideration of

I am having a hard time understanding how to perform a "repeated measures" type of ANOVA with R. When reading the document found here: http://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_repms.html I find that there is a reference to a function make.rm () that is supposed to rearrange a "one row per person" type of frame to a "one row per observation" type

I am perfectly aware that this question is not an R question, at least not yet, but I have not succeeded in finding what I want in other ways, so ... What I am looking for are two algorithms, preferabley in Pascal, but other languages may do. For (a) systematic (complete) permutations for grouped data with unequal group sizes, and (b) random permutations for the same kind of data. I know

Hi there, Suppose mu is constant, and error is normally distributed with mean 0 and fixed variance s. I need to find a statistics that: Y_i = mu + beta1* I1_i beta2*I2_i + beta3*I1_i*I2_i + +error, where I_i is 1 Y_i is from group A, and 0 if Y_i is from group B. It is large when beta1=beta2=0 It is small when beta1 and/or beta2 is not equal to 0 How can I find it by R? Thank you very much

Hi all, I am facing difficulty on how to use bootstrap sampling and below is my example of function. Read a data , use some functions and use iteration to find the solution( ie, convergence is reached). I want to use bootstrap approach to do it several times (200 or 300 times) this whole process and see the distribution of parameter of interest. Below is a small example that resembles my

I apologize for this post. I am new to R (two days) and I have tried and tried to calculated confidence intervals for medians. Can someone help me? Here is my data: institution1 0.21 0.16 0.32 0.69 1.15 0.9 0.87 0.87 0.73 The first four observations compose group 1 and observations 5 through 9 compose group 2. I would like to create a bootstrapped 90% confidence interval on the difference of

Hello, We are a group of PhD students working in the field of toxicology. Several of us have small data sets with N=10-15. Our research is mainly about the association between an exposure and an effect, so preferrably we would like to use linear regression models. However, most of the time our data do not fulfill the model assumptions for linear models ( no normality of y-varible achieved even

Hi all, I am trying to reproduce rails 3.2 behaviour with fields_for and nested attributes. class ControllerAction < ActiveRecord::Base has_many :interactions, dependent: :destroy has_many :roles, through: :interactions scope :controllers, lambda {|name| where("controller_name_id = ?", name)} scope :actions, lambda {|name| where("action_name_id =

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

The PDF file on the web, which is an appendix on nonlinear regression associated with the CAR book, is very nice. When I ran through the code presented there, I found something odd. The code does a certain model in 3 ways: Vanilla NLS (using numerical differentation), Analytical derivatives (where the user supplies the derivatives) and analytical derivatives (using automatic differentiation). The

Hello All, Recently, I was asked to help out with an SEM cross-validation analysis. Initially, the project was based on "sample-splitting" where half of cases were randomly assigned to a training sample and half to a testing sample. Attempts to replicate a model developed in the training sample using the testing sample were not entirely successful. A number of parameter estimates were

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

Dear All, May I seek your suggestion on a simple issue. I want to draw vertical lines at some positions in the following R plot. To be more specific, I wish to draw vertical lines at d=c(5.0,5.5,6) and they should go till p=c(0.12,0.60,0.20) . I haven't found any way out, though made several attempts. Please run the following commands first if you are interested in!

The cautions people have given about starting values are worth heeding. That nlxb() does well in many cases is useful, but not foolproof. And John Fox has shown that the problem can be tackled very simply too. Best, JN On 2023-08-19 18:42, Paul Bernal wrote: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El

Hi, I hope somebody can help me on how to use the hypergeometric function. I did read through the R documentation on hypergeometric but not really sure what it means. I would like to evaluate the hypergeometric function as follows: F((2*alpha+1)/2, (2*alpha+2)/2 , alpha+1/2, betasq/etasq). I'm not sure which function should be used- either phyper or qhyper or dhyper Where

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

hi all, I need to generate a function inside a loop: tmp is an array for (i in 1:10) { func<- func * function(beta1) dweibull(tmp[i],beta1,eta) } because then i need to integrate this function on beta. I could have written this : func<-function(beta1) prod(dweibull(tmp,beta1,eta)) (with eta and beta1 set) but it is unplottable and no integrable... i could make it a bit different but

Dear all, I have the following data set. V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 alpha beta 1 11 1 11 1 11 1 11 1 11 alpha beta1 2 12 2 12 2 12 2 12 2 12 alpha beta1 3 13 3 13 3 13 3 13 3 13 alpha beta1 4 14 4 14 4 14 4 14 4 14 alpha beta1 5 15 5 15 5 15 5 15 5